Abstract
We study local algorithms for sensor selection, in which each sensor in a network uses information from nearby sensors alone to decide if it should be selected to predict the data of non-selected sensors. Our goal is to show how the prediction quality can be improved by increasing the level of knowledge available to each sensor. We specifically study this for a graph model of the network, in which prediction quality is defined by virtual links between sensors. Each node knows the links along all paths of fixed length extending outward from itself. The maximum path length increases with the level of knowledge. We designed algorithms for the first few levels and evaluated them on randomly generated graphs and real datasets, determining the optimal parameters for each algorithm and comparing them to baseline global strategies. Our results show that just knowing the links to immediate neighbors is enough to be as good as a simple global greedy algorithm, and increasing the knowledge improves the selection quality.
| Original language | English |
|---|---|
| Title of host publication | PE-WASUN 2018 - Proceedings of the 15th ACM International Symposium on Performance Evaluation of Wireless Ad Hoc, Sensor, and Ubiquitous Networks |
| Publisher | Association for Computing Machinery, Inc |
| Pages | 84-91 |
| Number of pages | 8 |
| ISBN (Electronic) | 9781450359610 |
| DOIs | |
| State | Published - 25 Oct 2018 |
| Externally published | Yes |
| Event | 15th ACM International Symposium on Performance Evaluation of Wireless Ad Hoc, Sensor, and Ubiquitous Networks, PE-WASUN 2018 - Montreal, Canada Duration: 28 Oct 2018 → 2 Nov 2018 |
Publication series
| Name | PE-WASUN 2018 - Proceedings of the 15th ACM International Symposium on Performance Evaluation of Wireless Ad Hoc, Sensor, and Ubiquitous Networks |
|---|
Conference
| Conference | 15th ACM International Symposium on Performance Evaluation of Wireless Ad Hoc, Sensor, and Ubiquitous Networks, PE-WASUN 2018 |
|---|---|
| Country/Territory | Canada |
| City | Montreal |
| Period | 28/10/18 → 2/11/18 |
Bibliographical note
Publisher Copyright:© 2018 Association for Computing Machinery.
Funding
Research was sponsored by the Army Research Laboratory and was accomplished under Cooperative Agreement Number W911NF-09-2-0053 (the ARL Network Science CTA). The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on.
| Funders | Funder number |
|---|---|
| Army Research Laboratory | W911NF-09-2-0053 |
Keywords
- Sensor selection; local algorithms